There are many conventional traffic detection systems. Conventional systems typically utilize a single sensor type, either in the roadway itself, or positioned at a roadside location or on traffic lights proximate to the roadway. The most common type of vehicular sensors are inductive coils, or loops, embedded in a road surface. Other existing systems utilize video cameras, radar sensors, acoustic sensors, or magnetometers, either in the road itself, or at either the side of a roadway or positioned higher above traffic to observe and detect vehicles in a desired area. Each of these sensors provide information used to determine a presence of vehicles in specific lanes in intersections, to provide information to traffic signal controllers for proper actuation.
These conventional detection systems are commonly set up with ‘virtual zones’, which are hand- or machine-drawn areas on an image, taken from data collected by a sensor, where objects may be moving or present. Traditionally, a vehicle passes through or stops in a zone, and these zones generate an “output” when an object is detected as passing through or resting within all or part of the zone.
Many existing detection systems are capable of detecting different types of vehicles or objects, such as cars, trucks, bicycles, motorcycles, pedestrians, etc. Some sensor types, along with the algorithms associated with them, are more suitable than others for differentiating between these types vehicles or objects, and therefore detection quality varies depending on the type of sensor system used. For example, some systems are unable to differentiate between objects of a similar type, for example between motorcycles and bicycles, as the sensor data is too similar for the system to algorithmically separate these types of objects.
Different sensor types also have different limitations due to their base technology, application, and operating environment. For example, Doppler-type radar can detect moving objects and identify their position and speed, but they cannot detect stopped objects. Loops and magnetometers can detect and classify stopped or moving vehicles, however, they cannot detect as accurately during periods of dense traffic. Additionally, loops and magnetometers cannot be installed without cutting or boring into the pavement.
Environmental conditions also play a part in sensor data quality. Heavy fog can restrict the distance at which a camera sensor can see the field of view. Similarly, certain conditions, such as rain, sleet, and ice, can reduce or completely impair the performance of a radar sensor.
Accordingly, there is a need for improvements in the performance of existing sensing systems for traffic detection. Combining the best features of two or more sensor types within the same detection system allows for improvements in optimal performance, irrespective of sensor limitations or environmental conditions.